Nisarg Patel, MD

@nxpatel

@UCSF facial/reconstructive surgeon. Researching AI for clinical reasoning. Cofounder @memorahealth (acq). Alum @ycombinator w18, @broadinstitute, @harvardmed.

San Francisco, CA

nisargapatel.com

Replies Reposts Media

Pinned
Nisarg Patel, MD
@nxpatel
Jun 30
1/ When expert clinicians see similar cases, they often reuse stable mental templates, known as diagnostic schemas. I built a way to test whether LLMs do the same. (Below: my study group thinking through a cardiogenic shock case during med school)
3
Nisarg Patel, MD
@nxpatel
Jul 15
✌🏽ICML
1
Nisarg Patel, MD
@nxpatel
Jul 9
Excited to try this (especially since OE has full text data)! For the last couple months I’d been routing at least half of my clinical questions into the CLI with my evidence-grader skill for the exact same reason; it’s hard to gauge the quality of evidence in these answers
OpenEvidence
@OpenEvidence
Jul 9
Some clinical questions have thirty years of randomized trials behind them. Some have four case reports. Answers on OpenEvidence now carry a grade for how much certainty the underlying evidence can support, using the same GRADE criteria Cochrane and the WHO apply to guidelines.
Nisarg Patel, MD
@nxpatel
Jul 6
Heading to Seoul for ICML! Presenting a Spotlight paper on Saturday at the Structured Data for Health workshop. If you work on AI for health/bio, AI safety, uncertainty calibration, or CoT faithfulness come say hello or DM me. 📍July 11, COEX Hall D2, 9:50-10:50AM
Nisarg Patel, MD
@nxpatel
Jun 30
1/ When expert clinicians see similar cases, they often reuse stable mental templates, known as diagnostic schemas. I built a way to test whether LLMs do the same. (Below: my study group thinking through a cardiogenic shock case during med school)
Nisarg Patel, MD
@nxpatel
Jun 22
In medicine, we’re already seeing startups expand into clinical decision support tools with read-access to the entire health record, which are often *decades* long per patient. Compressing not only text, but imaging, labs/trajectory, and reasoning in the least lossy way
Vishnu
@VishnuKal
Jun 22
I've been researching how to compress coding agent context at the representation level, working directly with models' internal vectors instead of summarizing text. So far, I've hit 2x+ compression while preserving most factual recall.
2